[R] itsadug:: plot_smooth and plot_diff

[Bert Gunter]

To be clear, I know nothing about bam; I just wanted to correct a
statistical error:

"Since the 95% confidence intervals overlap, I would assume that there is no
difference in accuracy between the two conditions."

That is false. You need to look at a CI for the difference.

As you appear to be confused about the statistical issues, I suggest
you post on a statistical site like stats.stackexchange.com or consult
a local statistician.

Cheers,
Bert


Bert Gunter

"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )


On Sun, Jun 12, 2016 at 7:03 AM, Fotis Fotiadis <fotisfotiadis at gmail.com> wrote:
> Hi all
>
> I am using bam to analyse the data from my experiment.
> It's a learning experiment, "acc" denotes accuracy and "cnd" denotes a
> within-subjects variable (with two levels, "label" and "ideo")."Ctrial" is
> centered trial (ranging from 1 to 288).
>
> The model is:
> bam(acc~ 1 + cnd + s(ctrial) + s(ctrial, sbj, bs = "fs", m = 1), data=data,
> family=binomial)
>
> The model doesn't include two different smooths (one for each condition)
> since including two smooths does not result to a more parsimonious model,
> according to following model comparison:
>> compareML(m0.2, m1.2)
> m0.2: acc ~ 1 + cnd + s(ctrial) + s(ctrial, sbj, bs = "fs", m = 1)
>
> m1.2: acc ~ 1 + cnd + s(ctrial, by = cnd) + s(ctrial, sbj, bs = "fs",
>     m = 1)
>
> Chi-square test of fREML scores
> -----
>   Model    Score Edf Chisq    Df   p.value Sig.
> 1  m0.2 10183.31   6
> 2  m1.2 10173.33   8 9.975 2.000 4.654e-05  ***
>
> AIC difference: -2.16, model m0.2 has lower AIC.
>
>
> So, I'm trying to assess if there's a difference in accuracy between the
> two conditions.
>
> When using the plot_smooth function, the model predictions are the ones
> shown in Fig.1.
> The code used is:
> plot_smooth(fm, view="ctrial",
> cond=list(cnd="pseudo"),main="Model",xaxt="n",
> xlab="Trial",ylab="Proportion Correct", lwd=2, las=2, rm.ranef=TRUE,
> rug=FALSE, shade=T, col="red" )
> plot_smooth(fm, view="ctrial", cond=list(cnd="ideo"), xaxt="n",
> rm.ranef=TRUE, rug=FALSE, shade=T, col="blue", add=T , lty=2, lwd=2)
> legend(x=0.8, y=1.5,legend=c('Label', 'Ideogram'),col=c('red', 'blue'),
> lty=c(1,2), bty="n", lwd=2)
>
> Since the 95% confidence intervals overlap, I would assume that there is no
> difference in accuracy between the two conditions.
>
> I am also using plot_diff to directly plot the difference:
> plot_diff(fm, view="ctrial",comp=list(cnd=c("pseudo", "ideo")),
> transform.view=dnrmlz,rm.ranef=T)
> (dnrmlz is a simple function to de-normalize trial)
>
> The output of the function is:
> Summary:
> * ctrial : numeric predictor; with 100 values ranging from -1.725936 to
> 1.725936.
> * sbj : factor; set to the value(s): aggmpo96. (Might be canceled as random
> effect, check below.)
> * NOTE : The following random effects columns are canceled: s(ctrial,sbj)
>
> * Note: x-values are transformed.
>             Significant
> 1 0.759461 - 288.240539
>
> So, it seems that accuracy in the label condition is higher compared to the
> ideo condition throughout the experiment.
> This result seems to contradict the previous one.
>
> I am obviously misinterpreting something.
> Any ideas on what am I doing wrong?
>
> Thank you in advance for your time,
> Fotis
>
>
>
>
>
>
>
> --
> PhD Candidate
> Department of Philosophy and History of Science
> University of Athens, Greece.
> http://users.uoa.gr/~aprotopapas/LLL/en/members.html#fotisfotiadis
>
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